Scopus记录号: 2-s2.0-85058840398
论文题名: Similarity of fast and slow earthquakes illuminated by machine learning
作者: Hulbert C. ; Rouet-Leduc B. ; Johnson P.A. ; Ren C.X. ; Rivière J. ; Bolton D.C. ; Marone C.
刊名: Nature Geoscience
ISSN: 17520894
出版年: 2019
卷: 12, 期: 1 起始页码: 69
结束页码: 74
语种: 英语
英文摘要: Tectonic faults fail in a spectrum of modes, ranging from earthquakes to slow slip events. The physics of fast earthquakes are well described by stick–slip friction and elastodynamic rupture; however, slow earthquakes are poorly understood. Key questions remain about how ruptures propagate quasi-dynamically, whether they obey different scaling laws from ordinary earthquakes and whether a single fault can host multiple slip modes. We report on laboratory earthquakes and show that both slow and fast slip modes are preceded by a cascade of micro-failure events that radiate elastic energy in a manner that foretells catastrophic failure. Using machine learning, we find that acoustic emissions generated during shear of quartz fault gouge under normal stress of 1–10 MPa predict the timing and duration of laboratory earthquakes. Laboratory slow earthquakes reach peak slip velocities of the order of 1 × 10−4 m s−1 and do not radiate high-frequency elastic energy, consistent with tectonic slow slip. Acoustic signals generated in the early stages of impending fast laboratory earthquakes are systematically larger than those for slow slip events. Here, we show that a broad range of stick–slip and creep–slip modes of failure can be predicted and share common mechanisms, which suggests that catastrophic earthquake failure may be preceded by an organized, potentially forecastable, set of processes. © 2018, This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply. 资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/122643
Appears in Collections: 气候变化事实与影响
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作者单位: Geophysics Group, Los Alamos National Laboratory, Los Alamos, NM, United States; Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA, United States; Department of Geosciences, Pennsylvania State University, University Park, PA, United States
Recommended Citation:
Hulbert C.,Rouet-Leduc B.,Johnson P.A.,et al. Similarity of fast and slow earthquakes illuminated by machine learning[J]. Nature Geoscience,2019-01-01,12(1)